Table 4

Factors predicting appropriate outcome of mystery shopping scenarios—binary logistic regression model output (Nagelkerke R2=0.60)

Variableβ coefficientOR95% CIP value
Lower boundUpper bound
Pharmacist involvement in interaction0.3981.4891.1111.9960.008**
Mystery shopping visit number (1–9)0.1101.1131.0161.2260.022*
Questioning score0.0621.0631.0501.077<0.001***
Legislative status of product requested0.1861.2040.4833.0000.691
Mystery shopper identified by pharmacy staff0.4051.5000.8752.5720.140
Individual pharmacy†−0.0100.9900.9681.0140.418
Allergic rhinitis scenario (reference scenario)<0.001***
Adult cough/cold scenario0.2111.2350.3814.0050.752
Adult pain scenario1.7545.7791.30725.5550.021*
Asthma scenario2.86817.5963.76282.301<0.001***
Diarrhoea scenario2.0727.9432.07130.4590.003**
Dyspepsia scenario2.90018.1824.46274.093<0.001***
Insomnia scenario1.2743.5760.81315.7400.092
Paediatric cough/cold scenario4.46386.77024.328309.478<0.001***
Paediatric fever scenario3.62337.43710.723130.699<0.001***
Smoking cessation scenario0.0241.0240.1139.2510.983
  • *Significant at 0.05 level.

  • **Significant at 0.01 level.

  • ***Significant at 0.001 level.

  • †Pharmacy was not broken down further due to not returning a significant value.